Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models.
View Article and Find Full Text PDFIntroduction: Treatment options for patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) with disease progression on/after osimertinib and platinum-based chemotherapy are limited.
Methods: CHRYSALIS-2 Cohort A evaluated amivantamab+lazertinib in patients with EGFR exon 19 deletion- or L858R-mutated NSCLC with disease progression on/after osimertinib and platinum-based chemotherapy. Primary endpoint was investigator-assessed objective response rate (ORR).
Background: Latinas suffer disproportionately from breast cancer, partially due to lower uptake of guideline-concordant breast cancer screening. We describe the design of a study to compare two approaches addressing this important public health problem.
Design/methods: We are conducting a 5-year randomized controlled trial.
Objectives: Targeted therapies have been shown to improve outcomes in metastatic non-small cell lung cancer (mNSCLC) with driver mutations. We evaluated the real-world prevalence of human epidermal growth factor receptor 2 (HER2; ERBB2) tumor gene mutations among patients with mNSCLC and described historical treatments and outcomes in patients with HER2-mutant mNSCLC, during a period when there was no approved targeted therapy for HER2-mutant mNSCLC.
Materials And Methods: This retrospective observational study used a US nationwide de-identified NSCLC clinico-genomic database.